Related papers: Memcapacitive Devices in Logic and Crossbar Applic…
The areal footprint of memristors is a key consideration in material-based neuromorophic computing and large-scale architecture integration. Electronic transport in the most widely investigated memristive devices is mediated by filaments,…
Memristors have been at the forefront of nanoelectronics research for the last decade, offering a valuable component to reconfigurable computing. Their attributes have been studied extensively along with applications that leverage their…
Traditional studies of memristive devices have mainly focused on their applications in non-volatile information storage and information processing. Here, we demonstrate that the third fundamental component of information technologies { the…
Memristive crossbars enable in-memory multiply-accumulate and local plasticity learning, offering a path to energy-efficient edge AI. To this end, we present Open-MENA (Open Memristor-in-Memory Accelerator), which, to our knowledge, is the…
Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device endurance have yet to be overcome. In this work, we…
Reservoir computing is a machine learning paradigm that uses a high-dimensional dynamical system, or \emph{reservoir}, to approximate and predict time series data. The scale, speed and power usage of reservoir computers could be enhanced by…
This paper examines the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in memristive devices, focusing on ReRAM devices, specifically the interface-type or non-filamentary analog switching devices. A…
Data-intensive computing tasks, such as training neural networks, are crucial for artificial intelligence applications but often come with high energy demands. One promising solution is to develop specialized hardware that directly maps…
Memristors are passive circuit elements which behave as resistors with memory. The recent experimental realization of a memristor has triggered interest in this concept and its possible applications. Here, we demonstrate memristive response…
The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure.…
Compute-In-Memory (CIM) systems, particularly those utilizing ReRAM and memristive technologies, offer a promising path toward energy-efficient neural network computation. However, conventional quantization and compression techniques often…
A Boltzmann machine whose effective "temperature" can be dynamically "cooled" provides a stochastic neural network realization of simulated annealing, which is an important metaheuristic for solving combinatorial or global optimization…
General purpose computing systems are used for a large variety of applications. Extensive supports for flexibility in these systems limit their energy efficiencies. Neural networks, including deep networks, are widely used for signal…
Current memcapacitor implementations typically demand complex fabrication processes or depend on organic materials exhibiting poor environmental stability and reproducibility. Here, we demonstrate memcapacitor structures utilizing a quasi…
A content-addressable-memory compares an input search word against all rows of stored words in an array in a highly parallel manner. While supplying a very powerful functionality for many applications in pattern matching and search, it…
Nanomagnetic logic, in which the outcome of a computation is embedded into the energy hierarchy of magnetostatically coupled nanomagnets, offers an attractive pathway to implement in-memory computation. This computational paradigm avoids…
The surge in AI usage demands innovative power reduction strategies. Novel Compute-in-Memory (CIM) architectures, leveraging advanced memory technologies, hold the potential for significantly lowering energy consumption by integrating…
In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is…
While two-terminal HfOX (x<2) memristor devices have been studied for ion transport and current evolution, there have been limited reports on the effect of the long-range thermal environment on their performance. In this work,…
Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive…